Computing Machines), Toronto: Canada (2012)" 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines Bus-based MPSoC security through communication protection: A latency-efficient
Bibliographic record
Abstract
Abstract—Security in MPSoC is gaining an increasing attention since several years. Digital convergence is one of the numerous reasons explaining such a focus on embedded systems as much sensitive and secret data are now stored, manipulated and exchanged in these systems. Most solutions are currently built at the software level; we believe hardware enhancements also play a major role in system protection. One strategic point is the communication layer as all data goes through it. Monitoring and controlling communications enable to fend off attacks before system corruption. In this work, we propose an efficient solution with several hardware enhancements to secure data exchanges in a bus-based MPSoC. Our approach relies on low complexity distributed firewalls connected to all critical IPs of the system. Designers can deploy different security policies (access right, data format, authentication, confidentiality) in order to protect the system in a flexible way. To illustrate the benefit of such a solution, implementations are discussed for different MPSoCs implemented on Xilinx Virtex-6 FPGAs. Results demonstrate a reduction up to 33 % in terms of latency overhead compared to existing efforts. Keywords-communication; security; MPSoC; bus; cryptography; external memory; firewall; latency I.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.213 | 0.073 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".